Search results for 'Forward models' (try it on Scholar)

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  1. Axel Cleeremans, Applying Forward Models to Sequence Learning: A Connectionist Implementation.score: 60.0
    The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed — and often implicit — sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations (...)
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  2. Jeffrey Bowers (2013). How Do Forward Models Work? And Why Would You Want Them? Behavioral and Brain Sciences 36 (4):349-350.score: 60.0
    The project of coordinating perception, comprehension, and motor control is an exciting one, but I found it hard to follow some of Pickering & Garrod's (P&G's) arguments as presented. Consequently, my comment is not so much a disagreement with P&G but a query about the logic of forward models: It is not clear how they are supposed to work, nor why they are needed in this (or many other) contexts, and toward that end I present an alternative idea.
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  3. Martin J. Pickering & Simon Garrod (2013). Forward Models and Their Implications for Production, Comprehension, and Dialogue. Behavioral and Brain Sciences 36 (4):377-392.score: 60.0
    Our target article proposed that language production and comprehension are interwoven, with speakers making predictions of their own utterances and comprehenders making predictions of other people's utterances at different linguistic levels. Here, we respond to comments about such issues as cognitive architecture and its neural basis, learning and development, monitoring, the nature of forward models, communicative intentions, and dialogue.
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  4. David Poeppel Xing Tian (2010). Mental Imagery of Speech and Movement Implicates the Dynamics of Internal Forward Models. Frontiers in Psychology 1.score: 60.0
    The classical concept of efference copies in the context of internal forward models has stimulated productive research in cognitive science and neuroscience. There are compelling reasons to argue for such a mechanism, but finding direct evidence in the human brain remains difficult. Here we investigate the dynamics of internal forward models from an unconventional angle: mental imagery, assessed while recording high temporal resolution neuronal activity using magnetoencephalography (MEG). We compare two overt and covert tasks; our covert, (...)
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  5. Robert J. Hartsuiker (2013). Are Forward Models Enough to Explain Self-Monitoring? Insights From Patients and Eye Movements. Behavioral and Brain Sciences 36 (4):357-358.score: 60.0
    At the core of Pickering & Garrod's (P&G's) theory is a monitor that uses forward models. I argue that this account is challenged by neuropsychological findings and visual world eye-tracking data and that it has two conceptual problems. I propose that conflict monitoring avoids these issues and should be considered a promising alternative to perceptual loop and forward modeling theories.
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  6. Robert Briscoe (2011). The Elusive Experience of Agency. Topics in Cognitive Science 3 (2):262-267.score: 45.0
    I here present some doubts about whether Mandik’s (2010) proposed intermediacy and recurrence constraints are necessary and sufficient for agentive experience. I also argue that in order to vindicate the conclusion that agentive experience is an exclusively perceptual phenomenon (Prinz, 2007), it is not enough to show that the predictions produced by forward models of planned motor actions are conveyed by mock sensory signals. Rather, it must also be shown that the outputs of “comparator” mechanisms that compare these (...)
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  7. Martin J. Pickering & Andy Clark (forthcoming). Getting Ahead: Forward Models and Their Place in Cognitive Architecture. Trends in Cognitive Sciences.score: 45.0
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  8. Xing Tian & David Poeppel (2010). Mental Imagery of Speech and Movement Implicates the Dynamics of Internal Forward Models. Frontiers in Psychology 1:166.score: 45.0
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  9. Michael I. Jordan & David E. Rumelhart (1992). Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16 (3):307-354.score: 45.0
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  10. D. M. Wolpert & J. R. Flanagan (2009). Forward Models. In Bayne Tim, Cleeremans Axel & Wilken Patrick (eds.), The Oxford Companion to Consciousness. Oxford University Press. 294--296.score: 45.0
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  11. André Lee, Shinichi Furuya, Matthias Karst & Eckart Altenmüller (2013). Alteration in Forward Model Prediction of Sensory Outcome of Motor Action in Focal Hand Dystonia. Frontiers in Human Neuroscience 7.score: 40.0
    Focal hand dystonia in musicians is a movement disorder affecting highly trained movements. Rather than being a pure motor disorder related to movement execution only, movement planning, error prediction and sensorimotor integration are also impaired. Internal models, of which two types, forward and inverse models have been described and most likely processed in the cerebellum, are known to be involved in these tasks. Recent results indicate that the cerebellum may be involved in the pathophysiology of focal dystonia. (...)
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  12. Gary S. Dell (2013). Cascading and Feedback in Interactive Models of Production: A Reflection of Forward Modeling? Behavioral and Brain Sciences 36 (4):351-352.score: 37.0
    Interactive theories of lexical retrieval in language production assume that activation cascades from earlier to later processing levels, and feeds back in the reverse direction. This commentary invites Pickering & Garrod (P&G) to consider whether cascading and feedback can be seen as a form of forwarding modeling within a hierarchical production system.
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  13. Sa'idu Sulaiman (1998). Islamization of Knowledge: Background, Models and the Way Forward. The International Institute of Islamic Thought.score: 36.0
    On the implementation aspect of the Islamization of knowledge programme, there were also suggestions that my paper should provide readers with Al-Faruqi's ...
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  14. Opher Donchin & Amir Raz (2004). Where in the Brain Does the Forward Model Lurk? Behavioral and Brain Sciences 27 (3):402-403.score: 36.0
    The general applicability of forward models in brain function has previously been recognized. Grush's contribution centers largely on broadening the extent and scope of forward models. However, in his effort to expand and generalize, important distinctions may have been overlooked. A better grounding in the underlying physiology would have helped to illuminate such valuable differences and similarities.
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  15. Stephen R. Carpenter & Lance H. Gunderson (2001). Coping with Collapse: Ecological and Social Dynamics in Ecosystem Management Like Flight Simulators That Train Would-Be Aviators, Simple Models Can Be Used to Evoke People's Adaptive, Forward-Thinking Behavior, Aimed in This Instance at Sustainability of Human–Natural Systems. Bioscience 51 (6):451-457.score: 36.0
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  16. Thomas G. Fikes & James T. Townsend (1995). Moving Models of Motion Forward: Explication and a New Concept. Behavioral and Brain Sciences 18 (4):751.score: 36.0
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  17. Keith Morrison (2010). A Review of “Indra's Net: Alchemy and Chaos Theory as Models for Transformation” Robertson, Robin (with a Forward by Allan Combs). Wheaton, IL: Quest Books, 2009 (Notes, Bibliography, Credit Illustrations and Index, 175 Pp., $16.95 USD, Paperback, ISBN: 978-0-8356-0862-6). [REVIEW] World Futures 66 (8):626-629.score: 36.0
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  18. Gary M. Oppenheim (2013). Inner Speech as a Forward Model? Behavioral and Brain Sciences 36 (4):369-370.score: 36.0
    Pickering & Garrod (P&G) consider the possibility that inner speech might be a product of forward production models. Here I consider the idea of inner speech as a forward model in light of empirical work from the past few decades, concluding that, while forward models could contribute to it, inner speech nonetheless requires activity from the implementers.
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  19. Frederic Peters (2010). Consciousness as Recursive, Spatiotemporal Self-Location. Psychological Research.score: 30.0
    At the phenomenal level, consciousness arises in a consistently coherent fashion as a singular, unified field of recursive self-awareness (subjectivity) with explicitly orientational characteristics—that of a subject located both spatially and temporally in an egocentrically-extended domain. Understanding these twin elements of consciousness begins with the recognition that ultimately (and most primitively), cognitive systems serve the biological self-regulatory regime in which they subsist. The psychological structures supporting self-located subjectivity involve an evolutionary elaboration of the two basic elements necessary for extending self-regulation (...)
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  20. Chiara Gambi & Martin J. Pickering (2013). Prediction and Imitation in Speech. Frontiers in Psychology 4.score: 30.0
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  21. Wolfgang Prinz Anne Springer, Jim Parkinson (2013). Action Simulation: Time Course and Representational Mechanisms. Frontiers in Psychology 4.score: 30.0
    The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others’ actions implies a 'mirroring' of their activities, based on one’s own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the (...)
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  22. Cyrill Guy Martin Ott & Lutz Jäncke (2013). Processing of Self-Initiated Speech-Sounds is Different in Musicians. Frontiers in Human Neuroscience 7.score: 29.0
    Musicians and musically untrained individuals have been shown to differ in a variety of functional brain processes such as auditory analysis and sensorimotor interaction. At the same time, internally operating forward models are assumed to enable the organism to discriminate the sensory outcomes of self-initiated actions from other sensory events by deriving predictions from efference copies of motor commands about forthcoming sensory consequences. As a consequence, sensory responses to stimuli that are triggered by a self-initiated motor act are (...)
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  23. Björn Kralemann & Claas Lattmann (2013). Models as Icons: Modeling Models in the Semiotic Framework of Peirce's Theory of Signs. Synthese 190 (16):3397-3420.score: 27.0
    In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) (...)
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  24. Myrto I. Mylopoulos & David Pereplyotchik (2013). Is There Any Evidence for Forward Modeling in Language Production? Behavioral and Brain Sciences 36 (4):368-369.score: 24.0
    The neurocognitive evidence that Pickering & Garrod (P&G) cite in favor of positing forward models in speech production is not compelling. The data to which they appeal either cannot be explained by forward models, or can be explained by a more parsimonious model.
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  25. Jeff Prideaux (1996). Feed-Forward Activation in a Theoretical First-Order Biochemical Pathway Which Contains an Anticipatory Model. Acta Biotheoretica 44 (3-4).score: 24.0
    This paper explores the consequences of the theoretical forward activation enzymatic pathway A 0 A 1 A 2 A 3 where E 1 convents A 0 to A 1, E 2 converts A 1 to A 2 and E 3 converts A 2 to A 3. A 0, which is environmentally determined, also serves to activate (or modulate) the activity of E 3 in such a way as to keep the concentration of A 2 ([A 2]) constant at a (...)
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  26. Kelly C. Strong & Rhonda Wiley Jones (2005). A Model for Feed-Forward Assessment of Student Learning in Industry-Issues Courses. Proceedings of the International Association for Business and Society 16:379-380.score: 24.0
    The validity of assessment programs is increasingly important in higher education. Existing approaches to assessment are problematic because they eitherfail to provide timely feedback or have suspect measurement issues. We propose a feed-forward assessment model to help overcome these two limitations oftraditional assessment approaches.
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  27. Steven French (2010). Keeping Quiet on the Ontology of Models. Synthese 172 (2):231 - 249.score: 23.0
    Stein once urged us not to confuse the means of representation with that which is being represented. Yet that is precisely what philosophers of science appear to have done at the meta-level when it comes to representing the practice of science. Proponents of the so-called ‘syntactic’ view identify theories as logically closed sets of sentences or propositions and models as idealised interpretations, or ‘theoruncula, as Braithwaite called them. Adherents of the ‘semantic’ approach, on the other hand, are typically characterised (...)
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  28. Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 21.0
    According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...)
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  29. S. Kaplan, M. Weaver & Robert M. French (1990). Active Symbols and Internal Models: Towards a Cognitive Connectionism. [REVIEW] AI and Society 4 (1):51-71.score: 21.0
    In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this (...)
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  30. Marion Vorms & David Lagnado, The Role of Models in Mind and Science.score: 21.0
    During the last few decades, models have become the centre of attention in both cognitive science and philosophy of science. In cognitive science, the claim that humans reason with mental models, rather than mentally manipulate linguistic symbols, is the majority view. Similarly, philosophers of science almost unanimously acknowledge that models have to be taken as a central unit of analysis. Moreover, some philosophers of science and cognitive scientists have suggested that the cognitive hypothesis of mental models (...)
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  31. Nelly Grosset & Pierre Barrouillet (2003). On the Nature of Mental Models of Conditional: The Case of If , If Then , and Only If. Thinking and Reasoning 9 (4):289 – 306.score: 21.0
    It has recently been reported that forward inferences from if p then q sentences (i.e., from antecedent to consequent) were faster than backward inferences from consequent to antecedent (Barrouillet, Grosset, & Lecas, 2000). The standard mental model theory assumes that this directionality effect is a figural effect due to the order the information enters working memory, whereas we claim that it results from the nature of the mental models that represent oriented relations from hypothetical values introduced by the (...)
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  32. Brendan Clarke, Bert Leuridan & Jon Williamson (2013). Modelling Mechanisms with Causal Cycles. Synthese:1-31.score: 21.0
    Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for (...)
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  33. Stéphane Demri & Dov Gabbay (2000). On Modal Logics Characterized by Models with Relative Accessibility Relations: Part II. Studia Logica 66 (3):349-384.score: 21.0
    This work is divided in two papers (Part I and Part II). In Part I, we introduced the class of Rare-logics for which the set of terms indexing the modal operators are hierarchized in two levels: the set of Boolean terms and the set of terms built upon the set of Boolean terms. By investigating different algebraic properties satisfied by the models of the Rare-logics, reductions for decidability were established by faithfully translating the Rare-logics into more standard modal logics (...)
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  34. Paul Croll, Dorothy Abbott, Patrica Broadfoot, Marilyn Osborn & Andrew Pollard (1994). Teachers and Education Policy: Roles and Models. British Journal of Educational Studies 42 (4):333 - 347.score: 21.0
    Four models are outlined for describing and analysing the role of teachers in the formulation of educational policy and the resulting processes of change. The model of teachers as partners in education policy making draws on a pluralist view of political processes and an assumption of a degree of autonomy for teachers and schools. A model of teachers as implementers of change draws a sharp distinction between the processes of policy making and policy execution and excludes teachers from an (...)
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  35. Gonzalo Olcina (1997). Forward Induction in Games with an Outside Option. Theory and Decision 42 (2):177-192.score: 21.0
    We provide eductive foundations for the concept of forward induction, in the class of games with an outside option. The formulation presented tries to capture in a static notion the rest point of an introspective process, achievable from some restricted preliminary beliefs. The former requisite is met by requiring the rest point to be a Nash equilibrium that yields a higher payoff than the outside option. With respect to the beliefs, we propose the Incentive Dominance Criterion. Players should consider (...)
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  36. Margaret Exley (2007). Managing CEO Succession: New Models for a New Era. International Journal of Business Governance and Ethics 3 (2):139-149.score: 21.0
    External pressures have changed the context within which CEOs are succeeded. At the same time, chairmen are clear that this responsibility is personal to them and are increasingly changing the nature of the process. Two new models of CEO succession are identified: one where the Board actively partners with the incumbent CEO and the other a crisis model where the Chairman and the Board assure the active management of the succession process. In both cases, best practice is for the (...)
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  37. B. Vereijken & H. T. A. Whiting (1998). Hoist by Their Own Petard: The Constraints of Hierarchical Models. Behavioral and Brain Sciences 21 (5):705-705.score: 21.0
    In the context of the motor skill literature on observational learning and hierarchical skill structuring, Byrne & Russon's findings call into question their standpoint that great apes imitate the behaviour of role models at the programme level. The authors impose a hierarchical model on their observations without properly considering alternative explanations. One such possibility, which stems from a constraints perspective that they dismiss, is put forward.
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  38. M. Schilling & H. Cruse (2011). What's Next: Recruitment of a Grounded Predictive Body Model for Planning a Robot's Actions. Frontiers in Psychology 3:383-383.score: 20.0
    Even comparatively simple, reactive systems are able to control complex motor tasks, such as hexapod walking on unpredictable substrate. The capability of such a controller can be improved by introducing internal models of the body and of parts of the environment. Such internal models can be applied as inverse models, as forward models or to solve the problem of sensor fusion. Usually, separate models are used for these functions. Furthermore, separate models are used (...)
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  39. Motoaki Sugiura (2013). Associative Account of Self-Cognition: Extended Forward Model and Multi-Layer Structure. Frontiers in Human Neuroscience 7.score: 20.0
    The neural correlates of “self” identified by neuroimaging studies differ depending on which aspects of self are addressed. Here, three categories of self are proposed based on neuroimaging findings and an evaluation of the likely underlying cognitive processes. The physical self, representing self-agency of action, body ownership, and bodily self-recognition, is supported by the sensory and motor association cortices located primarily in the right hemisphere. The interpersonal self, representing the attention or intentions of others directed at the self, is supported (...)
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  40. H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer. 263-280.score: 19.0
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
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  41. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.score: 18.0
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by (...)
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  42. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.score: 18.0
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
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  43. Stathis Psillos (2011). Living with the Abstract: Realism and Models. Synthese 180 (1):3 - 17.score: 18.0
    A natural way to think of models is as abstract entities. If theories employ models to represent the world, theories traffic in abstract entities much more widely than is often assumed. This kind of thought seems to create a problem for a scientific realist approach to theories. Scientific realists claim theories should be understood literally. Do they then imply (and are they committed to) the reality of abstract entities? Or are theories simply—and incurably—false (if there are no abstract (...)
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  44. Arnon Levy & Adrian Currie (forthcoming). Model Organisms Are Not (Theoretical) Models. British Journal for the Philosophy of Science:axt055.score: 18.0
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different (...)
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  45. Adam Toon (2010). Models as Make-Believe. In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.score: 18.0
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, (...)
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  46. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.score: 18.0
    I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of (...)
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  47. Daniel A. Weiskopf (2011). Models and Mechanisms in Psychological Explanation. Synthese 183 (3):313-338.score: 18.0
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of (...)
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  48. Rasmus Grønfeldt Winther (2006). On the Dangers of Making Scientific Models Ontologically Independent: Taking Richard Levins' Warnings Seriously. Biology and Philosophy 21 (5):703-724.score: 18.0
    Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare the positions (...)
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  49. Thomas Mormann, McKinsey Algebras and Topological Models of S4.1.score: 18.0
    The aim of this paper is to show that every topological space gives rise to a wealth of topological models of the modal logic S4.1. The construction of these models is based on the fact that every space defines a Boolean closure algebra (to be called a McKinsey algebra) that neatly reflects the structure of the modal system S4.1. It is shown that the class of topological models based on McKinsey algebras contains a canonical model that can (...)
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  50. Sylvia Wenmackers & Danny E. P. Vanpoucke (2012). Models and Simulations in Material Science: Two Cases Without Error Bars. Statistica Neerlandica 66 (3):339–355.score: 18.0
    We discuss two research projects in material science in which the results cannot be stated with an estimation of the error: a spectroscopic ellipsometry study aimed at determining the orientation of DNA molecules on diamond and a scanning tunneling microscopy study of platinum-induced nanowires on germanium. To investigate the reliability of the results, we apply ideas from the philosophy of models in science. Even if the studies had reported an error value, the trustworthiness of the result would not depend (...)
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